Uncovering Patient and Caregiver Goals for Goal-Concordant Care in Kidney Therapy Decisions.

CONTEXT: In kidney therapy (KT) decisions, goal-concordant decision-making is recognized to be important, yet alignment with patients' goals during dialysis initiation is not always achieved. OBJECTIVES: To explore older patients' and caregivers' hopes, goals, and fears related to KT and communication of these elements with members of their health care team. METHODS: The study included patients aged ≥75 years with an estimated glomerular filtration rate ≤25 mL/min/1.73 m2 and their caregivers enrolled in a palliative care intervention for KT decision-making. Patients and caregivers were asked open-ended questions about their hopes, goals, and fears related to KT decisions. A survey assessed if patients shared their goals with members of their health care team. Qualitative data underwent content analysis, supplemented by demographic descriptive statistics. RESULTS: The mean age of patients (n = 26) was 82.7 (±5.7) years, and caregivers (n = 15) had a mean age of 66.4 (±13.7) years. Among the participants, 13 patients and 11 caregivers were women, and 20 patients and 12 caregivers were White. Four themes emerged: (1) Maintaining things as good as they are by avoiding dialysis-related burdens; (2) seeking longevity while avoiding dialysis; (3) avoiding pain, symptoms, and body disfigurement; and (4) deferring decision-making. Patients rarely had shared their goals with the key members of their health care team. CONCLUSION: Patients and caregivers prioritize maintaining quality of life, deferring decision-making regarding dialysis, and avoiding dialysis-related burdens. These goals are often unshared with their family and health care teams. Given our aging population, urgent action is needed to educate clinicians to actively explore and engage with patient goals in KT decision-making.

PVTAD: ALZHEIMER'S DISEASE DIAGNOSIS USING PYRAMID VISION TRANSFORMER APPLIED TO WHITE MATTER OF T1-WEIGHTED STRUCTURAL MRI DATA.

Alzheimer's disease (AD) is a neurodegenerative disorder, and timely diagnosis is crucial for early interventions. AD is known to have disruptive local and global brain neural connections that may be instrumental in understanding and extracting specific biomarkers. Previous machine-learning approaches are mostly based on convolutional neural network (CNN) and standard vision transformer (ViT) models which may not sufficiently capture the multidimensional local and global patterns that may be indicative of AD. Therefore, in this paper, we propose a novel approach called PVTAD to classify AD and cognitively normal (CN) cases using pretrained pyramid vision transformer (PVT) and white matter (WM) of T1-weighted structural MRI (sMRI) data. Our approach combines the advantages of CNN and standard ViT to extract both local and global features indicative of AD from the WM coronal middle slices. We performed experiments on subjects with T1-weighed MPRAGE sMRI scans from the ADNI dataset. Our results demonstrate that the PVTAD achieves an average accuracy of 97.7% and F1-score of 97.6%, outperforming the single and parallel CNN and standard ViT architectures based on sMRI data for AD vs. CN classification.

GPU-acceleration of the distributed-memory database peptide search of mass spectrometry data.

Database peptide search is the primary computational technique for identifying peptides from the mass spectrometry (MS) data. Graphical Processing Units (GPU) computing is now ubiquitous in the current-generation of high-performance computing (HPC) systems, yet its application in the database peptide search domain remains limited. Part of the reason is the use of sub-optimal algorithms in the existing GPU-accelerated methods resulting in significantly inefficient hardware utilization. In this paper, we design and implement a new-age CPU-GPU HPC framework, called GiCOPS, for efficient and complete GPU-acceleration of the modern database peptide search algorithms on supercomputers. Our experimentation shows that the GiCOPS exhibits between 1.2 to 5[Formula: see text] speed improvement over its CPU-only predecessor, HiCOPS, and over 10[Formula: see text] improvement over several existing GPU-based database search algorithms for sufficiently large experiment sizes. We further assess and optimize the performance of our framework using the Roofline Model and report near-optimal results for several metrics including computations per second, occupancy rate, memory workload, branch efficiency and shared memory performance. Finally, the CPU-GPU methods and optimizations proposed in our work for complex integer- and memory-bounded algorithmic pipelines can also be extended to accelerate the existing and future peptide identification algorithms. GiCOPS is now integrated with our umbrella HPC framework HiCOPS and is available at: https://github.com/pcdslab/gicops .

Confounding Effects on the Performance of Machine Learning Analysis of Static Functional Connectivity Computed from rs-fMRI Multi-site Data.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the functional connectivity of the human brain. While rs-fMRI multi-site data can help to understand the inner working of the brain, the data acquisition and processing of this data has many challenges. One of the challenges is the variability of the data associated with different acquisitions sites, and different MRI machines vendors. Other factors such as population heterogeneity among different sites, with variables such as age and gender of the subjects, must also be considered. Given that most of the machine-learning models are developed using these rs-fMRI multi-site data sets, the intrinsic confounding effects can adversely affect the generalizability and reliability of these computational methods, as well as the imposition of upper limits on the classification scores. This work aims to identify the phenotypic and imaging variables producing the confounding effects, as well as to control these effects. Our goal is to maximize the classification scores obtained from the machine learning analysis of the Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI multi-site data. To achieve this goal, we propose novel methods of stratification to produce homogeneous sub-samples of the 17 ABIDE sites, as well as the generation of new features from the static functional connectivity values, using multiple linear regression models, ComBat harmonization models, and normalization methods. The main results obtained with our statistical models and methods are an accuracy of 76.4%, sensitivity of 82.9%, and specificity of 77.0%, which are 8.8%, 20.5%, and 7.5% above the baseline classification scores obtained from the machine learning analysis of the static functional connectivity computed from the ABIDE rs-fMRI multi-site data.

Development and Acceptability of a Kidney Therapy Decision Aid for Patients Aged 75 Years and Older: A Design-Based Research Involving Patients, Caregivers, and a Multidisciplinary Team.

Rationale & Objective: Many older adults prefer quality of life over longevity, and some prefer conservative kidney management (CKM) over dialysis. There is a lack of patient-decision aids for adults aged 75 years or older facing kidney therapy decisions, which not only include information on dialysis and CKM but also encourage end-of-life planning. We iteratively developed a paper-based patient-decision aid for older people with low literacy and conducted surveys to assess its acceptability. Study Design: Design-based research. Setting and Participants: Informed by design-based research principles and theory of behavioral activation, a multidisciplinary team of experts created a first version of the patient-decision aid containing 2 components: (1) educational material about kidney therapy options such as CKM, and (2) a question prompt list relevant to kidney therapy and end-of-life decision making. On the basis of the acceptability input of patients and caregivers, separate qualitative interviews of 35 people receiving maintenance dialysis, and with the independent feedback of educated layperson, we further modified the patient-decision aid to create a second version. Analytical Approach: We used descriptive statistics to present the results of acceptability surveys and thematic content analyses for patients' qualitative interviews. Results: The mean age of patients (n=21) who tested the patient-decision aid was 80 years and the mean age of caregivers (n=9) was 70 years. All respondents held positive views about the educational component and would recommend the educational component to others (100% patients and caregivers). Most of the patients reported that the question prompt list helped them put concerns into words (80% patients and 88% caregivers) and would recommend the question prompt list to others (95% patients and 100% caregivers). Limitations: Single-center study. Conclusions: Both components of the patient-decision aid received high acceptability ratings. We plan to launch a larger effectiveness study to test the outcomes of a decision-supporting intervention combining the patient-decision aid with palliative care-based decision coaching.

PPAD: a deep learning architecture to predict progression of Alzheimer's disease.

MOTIVATION: Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal state and AD. Not all people who have MCI convert to AD. The diagnosis of AD is made after significant symptoms of dementia such as short-term memory loss are already present. Since AD is currently an irreversible disease, diagnosis at the onset of the disease brings a huge burden on patients, their caregivers, and the healthcare sector. Thus, there is a crucial need to develop methods for the early prediction AD for patients who have MCI. Recurrent neural networks (RNN) have been successfully used to handle electronic health records (EHR) for predicting conversion from MCI to AD. However, RNN ignores irregular time intervals between successive events which occurs common in electronic health record data. In this study, we propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer's Disease (PPAD) and PPAD-Autoencoder. PPAD and PPAD-Autoencoder are designed for early predicting conversion from MCI to AD at the next visit and multiple visits ahead for patients, respectively. To minimize the effect of the irregular time intervals between visits, we propose using age in each visit as an indicator of time change between successive visits. RESULTS: Our experimental results conducted on Alzheimer's Disease Neuroimaging Initiative and National Alzheimer's Coordinating Center datasets showed that our proposed models outperformed all baseline models for most prediction scenarios in terms of F2 and sensitivity. We also observed that the age feature was one of top features and was able to address irregular time interval problem. AVAILABILITY AND IMPLEMENTATION: https://github.com/bozdaglab/PPAD.

Description of Dissolved Organic Matter Transformational Networks at the Molecular Level.

Dissolved Organic Matter (DOM) is an important component of the global carbon cycle. Unscrambling the structural footprint of DOM is key to understand its biogeochemical transformations at the mechanistic level. Although numerous studies have improved our knowledge of DOM chemical makeup, its three-dimensional picture remains largely unrevealed. In this work, we compare four solid phase extracted (SPE) DOM samples from three different freshwater ecosystems using high resolution mobility and ultrahigh-resolution Fourier transform ion cyclotron resonance tandem mass spectrometry (FT-ICR MS/MS). Structural families were identified based on neutral losses at the level of nominal mass using continuous accumulation of selected ions-collision induced dissociation (CASI-CID)FT-ICR MS/MS. Comparison of the structural families indicated dissimilarities in the structural footprint of this sample set. The structural family representation using Cytoscape software revealed characteristic clustering patterns among the DOM samples, thus confirming clear differences at the structural level (Only 10% is common across the four samples.). The analysis at the level of neutral loss-based functionalities suggests that hydration and carboxylation are ubiquitous transformational processes across the three ecosystems. In contrast, transformation mechanisms involving methoxy moieties may be constrained in estuarine systems due to extensive upstream lignin biodegradation. The inclusion of the isomeric content (mobility measurements at the level of chemical formula) in the structural family description suggests that additional transformation pathways and/or source variations are possible and account for the dissimilarities observed. While the structural character of more and diverse types of DOM samples needs to be assessed and added to this database, the results presented here demonstrate that Graph-DOM is a powerful tool capable of providing novel information on the DOM chemical footprint, based on structural interconnections of precursor molecules generated by fragmentation pathways and collisional cross sections.

PPAD: A deep learning architecture to predict progression of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease that affects millions of people worldwide. Mild cognitive impairment (MCI) is an intermediary stage between cognitively normal (CN) state and AD. Not all people who have MCI convert to AD. The diagnosis of AD is made after significant symptoms of dementia such as short-term memory loss are already present. Since AD is currently an irreversible disease, diagnosis at the onset of disease brings a huge burden on patients, their caregivers, and the healthcare sector. Thus, there is a crucial need to develop methods for the early prediction AD for patients who have MCI. Recurrent Neural Networks (RNN) have been successfully used to handle Electronic Health Records (EHR) for predicting conversion from MCI to AD. However, RNN ignores irregular time intervals between successive events which occurs common in EHR data. In this study, we propose two deep learning architectures based on RNN, namely Predicting Progression of Alzheimer's Disease (PPAD) and PPAD-Autoencoder (PPAD-AE). PPAD and PPAD-AE are designed for early predicting conversion from MCI to AD at the next visit and multiple visits ahead for patients, respectively. To minimize the effect of the irregular time intervals between visits, we propose using age in each visit as an indicator of time change between successive visits. Our experimental results conducted on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets showed that our proposed models outperformed all baseline models for most prediction scenarios in terms of F2 and sensitivity. We also observed that the age feature was one of top features and was able to address irregular time interval problem.

Racial Disparities in Health Beliefs and Advance Care Planning Among Patients Receiving Maintenance Dialysis.

CONTEXT: Among people receiving maintenance dialysis, little is known about racial disparities in the occurrence of prognostic discussions, beliefs about future health, and completion of advance care planning (ACP) documents. OBJECTIVES: We examined whether Black patients receiving maintenance dialysis differ from White patients in prognostic discussions, beliefs about future health, and completion of ACP-related documents. METHODS: We surveyed adult patients receiving maintenance dialysis from seven dialysis units in Cleveland, Ohio, and hospitalized patients at a tertiary care hospital in Cleveland. Of the 450 patients who were asked to participate in the study, 423 (94%) agreed. We restricted the current secondary analyses to include only Black (n=285) and White (n=114) patients. The survey assessed patients' knowledge of their kidney disease, attitudes toward chronic kidney disease (CKD) treatment, preferences for end-of-life (EoL) care, the patient-reported occurrence of prognostic discussions, experiences with kidney therapy decision making, sentiments of dialysis regret, beliefs about health over the next 12 months, and advance care planning. We used stepwise logistic regression to determine if race was associated with the occurrence of prognostic discussions, beliefs about future health, and completion of an ACP-related document, while controlling for potential confounders. RESULTS: We found no significant difference in the frequency of prognostic discussions between Black (11.9%) versus White patients (7%) (P=0.15). However, Black patients (19%) had lower odds of believing that their health would worsen over the next 12 months (OR 0.22, CI 0.12, 0.44) and reporting completion of any ACP-related document (OR 0.5, CI 0.32, 0.81) compared to White patients CONCLUSION: Racial differences exist in beliefs about future health and completion of ACP-related documents. Systemic efforts to investigate differences in health beliefs and address racial disparities in the completion of ACP-related documents are needed.

Dialysis Decision Making, Dialysis Experiences, and Illness Perceptions: A Qualitative Study of Pakistani Patients Receiving Maintenance Hemodialysis.

Rationale & Objective: The incidence and prevalence of patients with kidney failure requiring dialysis are increasing in Pakistan. However, in-depth perspectives on kidney care from Pakistani people requiring maintenance dialysis are lacking. Study Design: Qualitative interview study. Setting & Participants: Between September 2020 and January 2021, we interviewed 20 adults receiving maintenance hemodialysis in 2 outpatient dialysis units in Pakistan. We asked open-ended questions to explore their experiences with various aspects of kidney care. Analytical Approach: We recorded, transcribed, and then, using a phenomenological approach, thematically analyzed interviews. Results: We observed the following 6 main themes: (1) Patients perceived various supernatural phenomena as causes of their illness and chose traditional medicine for chronic kidney disease (CKD) treatment. (2) Patients expressed dissatisfaction with their physicians' communication. They felt poorly informed and resented their decision to initiate dialysis. (3) Family members tried to dissuade patients away from dialysis but also provided support once dialysis was initiated. (4) Patients and families found it challenging to afford dialysis and transplantation and also to arrange for transportation. (5) Women found it challenging to fulfill their obligations as wives and mothers while receiving maintenance dialysis. (6) Patients seemed reluctant to discuss end-of-life care. Limitations: We collected data from only 2 hospitals in neighboring cities. Additionally, patients on peritoneal dialysis were not included. Conclusions: Our findings shed light on patients' perspectives on kidney care in Pakistan and call for financially feasible solutions to raise kidney disease awareness and improve patients' experiences with dialysis. Physician training in communication and shared dialysis decision making along with the development of culturally adapted decision aids are needed to improve CKD knowledge and shared decision making. Although financial challenges preclude many from receiving long-term dialysis, cost-effective strategies to improve the availability of other options (eg, supportive kidney care, peritoneal dialysis, and transplantation) are still warranted.

Dialysis Organization Online Information on Kidney Failure Treatments: A Content Analysis Using Corpus Linguistics.

Rationale & Objective: Dialysis organizations' websites may influence patient decision making, but the websites have received almost no consideration. We investigated how/whether these websites present all kidney replacement therapy options and how the quality of life of these options is portrayed. Study Design: Content analysis using corpus linguistics (computer-assisted language analysis). Setting: Website content aimed at patients from the 2 major dialysis organizations' websites, totaling 226,968 words. The analysis took place from November 12, 2020, to March 30, 2021. Analytical Approach: We used linguistic software (AntConc) to document the frequencies of words needed to present treatment options and quality of life information. Results: Over both sites, dialysis mentions outstripped transplantation mentions. Organization A did not appear to reference conservative kidney management. Organization B mentioned dialysis more often than conservative management, at a ratio of 34:1. Organization A did not attribute symptoms to dialysis, whereas organization B had 12 mentions of dialysis-induced symptoms out of 87 total symptom references. Both organizations framed life on dialysis optimistically, suggesting that patients can continue to engage in "work," "sex," or "travel"; organization A referenced sex, work, and/or travel 123 times and organization B referenced these 262 times. Limitations: We used quantitative analysis and linked ideas with certain keywords. We did not conduct a detailed qualitative inquiry. Conclusions: The websites emphasized dialysis as a treatment for kidney failure, and the quality of life on dialysis was framed very optimistically. Qualitative studies of treatment modalities and the quality of life on dialysis in the patient-targeted material of dialysis organizations are needed.

Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions.

This paper provides a comprehensive literature review of various technologies and protocols used for medical Internet of Things (IoT) with a thorough examination of current enabling technologies, use cases, applications, and challenges. Despite recent advances, medical IoT is still not considered a routine practice. Due to regulation, ethical, and technological challenges of biomedical hardware, the growth of medical IoT is inhibited. Medical IoT continues to advance in terms of biomedical hardware, and monitoring figures like vital signs, temperature, electrical signals, oxygen levels, cancer indicators, glucose levels, and other bodily levels. In the upcoming years, medical IoT is expected replace old healthcare systems. In comparison to other survey papers on this topic, our paper provides a thorough summary of the most relevant protocols and technologies specifically for medical IoT as well as the challenges. Our paper also contains several proposed frameworks and use cases of medical IoT in hospital settings as well as a comprehensive overview of previous architectures of IoT regarding the strengths and weaknesses. We hope to enable researchers of multiple disciplines, developers, and biomedical engineers to quickly become knowledgeable on how various technologies cooperate and how current frameworks can be modified for new use cases, thus inspiring more growth in medical IoT.

Unsupervised Structural Classification of Dissolved Organic Matter Based on Fragmentation Pathways.

Dissolved organic matter (DOM) is considered an essential component of the Earth's ecological and biogeochemical processes. Structural information of DOM components at the molecular level remains one of the most extraordinary analytical challenges. Advances in determination of chemical formulas from the molecular studies of DOM have provided limited indications on structural signatures and potential reaction pathways. In this work, we extend the structural characterization of a wetland DOM sample using precursor and fragment molecular ions obtained by a sequential electrospray ionization-Fourier transform-ion cyclotron resonance tandem mass spectrometry (ESI-FT-ICR CASI-CID MS/MS) approach. The DOM chemical complexity resulted in near 900 precursors (P) and 24 000 fragment (F) molecular ions over a small m/z 261-477 range. The DOM structural content was dissected into families of structurally connected precursors based on neutral mass loss patterns (Pn-1 + F1:n + C) across the two-dimensional (2D) MS/MS space. This workflow identified over 1900 structural families of DOM compounds based on a precursor and neutral loss (H2O, CH4O, and CO2). The inspection of structural families showed a high degree of isomeric content (numerous identical fragmentation pathways), not discriminable with sole precursor ion analysis. The connectivity map of structural families allows for the visualization of potential biogeochemical processes that DOM undergoes throughout its lifetime. This study illustrates that integrating effective computational tools on a comprehensive high-resolution mass fragmentation strategy further enables the DOM structural characterization.

Outcomes Following In-Hospital Cardiopulmonary Resuscitation in People Receiving Maintenance Dialysis.

RATIONALE & OBJECTIVE: Previous studies showing poor cardiopulmonary resuscitation (CPR) outcomes in the dialysis population have largely been derived from claims data and are somewhat limited by a lack of detailed characterization of CPR events. We aimed to analyze CPR-related outcomes in individuals receiving maintenance dialysis. STUDY DESIGN: Retrospective chart review. SETTING & PARTICIPANTS: Using electronic medical records from a single academic health care system, we identified all hospitalized adult patients receiving maintenance dialysis who had undergone in-hospital CPR between 2006 and 2014. EXPOSURE: Initial in-hospital CPR. OUTCOMES: Overall survival, predictors of unsuccessful CPR, predictors of death during the same hospitalization among initial survivors, predictors of discharge-to-home status. ANALYTICAL APPROACH: We provide descriptive statistics for the study variables and used t tests, χ2 tests, or Fisher exact tests to compare differences between the groups. We built multivariable logistic regression models to examine the CPR-related outcomes. RESULTS: A total of 184 patients received in-hospital CPR: 51 (28%) did not survive the initial CPR event, and 77 CPR survivors died (additional 42%) later during the same hospitalization (overall mortality 70%). Only 18 (10%) were discharged home, with the remaining 32 (17%) discharged to a rehabilitation facility or a nursing home. In the multivariable model, the only predictor of unsuccessful CPR was CPR duration (OR, 1.41; 95% CI, 1.24-1.61; P < 0.001). Predictors of death during the same hospitalization after surviving the initial CPR event were CPR duration (OR, 1.15; 95% CI 1.04-1.27; P = 0.007) and older age (OR, 1.64; 95% CI, 1.23-2.2; P < 0.001). Older people also had lower odds of discharge-to-home status (OR, 0.25; 95% CI, 0.11-0.54; P < 0.001). LIMITATIONS: Retrospective study design, single-center study, no information on functional status. CONCLUSIONS: Patients receiving maintenance dialysis experience high mortality following in-hospital CPR and only 10% are discharged home. These data may help clinicians provide useful prognostic information while engaging in goals of care conversations.